Check three column condtion and delete based on condition
Question:
I have following dataframe df in pandas
item purchase_date purchase_qty purchase_price other_adjustments sold
0 0040030 2022-01 0 0.00 0 0.0
1 0050064 2022-01 0 0.00 -5 854.0
2 0050066 2022-01 0 0.00 2979 0.0
3 0050202 2022-01 0 0.00 14673 1320.0
4 0050204 2022-01 0 0.00 2538 0.0
I need to delete rows if all purchase_qty,other_adjustments and sold is 0.
I tried this
test_df = df[(df['purchase_qty'] != 0) & (df['other_adjustments'] != 0) & (df['sold'] != 0)]
This code delete all purchase_qty where it’s value is 0 but what I want is to check those 3 column and if all three are 0 then delete. Please help me
Answers:
You can filter 3 columns if at least one value is not 0 – so deleted rows with all 0 in 3 columns:
test_df = df[df[['purchase_qty','other_adjustments','sold']].ne(0).any(axis=1)]
print (test_df)
item purchase_date purchase_qty purchase_price other_adjustments
1 50064 2022-01 0 0.0 -5
2 50066 2022-01 0 0.0 2979
3 50202 2022-01 0 0.0 14673
4 50204 2022-01 0 0.0 2538
sold
1 854.0
2 0.0
3 1320.0
4 0.0
You need use or condition
test_df = df[(df['purchase_qty'] != 0) | (df['other_adjustments'] != 0) | (df['sold'] != 0)]
Or you can do an inverse operation
test_df = df[~((df['purchase_qty'] == 0) & (df['other_adjustments'] == 0) & (df['sold'] == 0))]
You can try this:
test_df = df[(df['purchase_qty'] != 0) | (df['other_adjustments'] != 0) | (df['sold'] != 0)]
This state: if one of these value is not 0 then keep it, otherwise, filter out.
This will work too!!
df = df[(df[['purchase_qty', 'other_adjustments', 'sold']] != 0).any(axis=1)]
This is a fix to your method used:
df = df.drop(df[(df.purchase_qty == 0) & (df.other_adjustments == 0) & (df.sold == 0.0)].index)
I have following dataframe df in pandas
item purchase_date purchase_qty purchase_price other_adjustments sold
0 0040030 2022-01 0 0.00 0 0.0
1 0050064 2022-01 0 0.00 -5 854.0
2 0050066 2022-01 0 0.00 2979 0.0
3 0050202 2022-01 0 0.00 14673 1320.0
4 0050204 2022-01 0 0.00 2538 0.0
I need to delete rows if all purchase_qty,other_adjustments and sold is 0.
I tried this
test_df = df[(df['purchase_qty'] != 0) & (df['other_adjustments'] != 0) & (df['sold'] != 0)]
This code delete all purchase_qty where it’s value is 0 but what I want is to check those 3 column and if all three are 0 then delete. Please help me
You can filter 3 columns if at least one value is not 0 – so deleted rows with all 0 in 3 columns:
test_df = df[df[['purchase_qty','other_adjustments','sold']].ne(0).any(axis=1)]
print (test_df)
item purchase_date purchase_qty purchase_price other_adjustments
1 50064 2022-01 0 0.0 -5
2 50066 2022-01 0 0.0 2979
3 50202 2022-01 0 0.0 14673
4 50204 2022-01 0 0.0 2538
sold
1 854.0
2 0.0
3 1320.0
4 0.0
You need use or condition
test_df = df[(df['purchase_qty'] != 0) | (df['other_adjustments'] != 0) | (df['sold'] != 0)]
Or you can do an inverse operation
test_df = df[~((df['purchase_qty'] == 0) & (df['other_adjustments'] == 0) & (df['sold'] == 0))]
You can try this:
test_df = df[(df['purchase_qty'] != 0) | (df['other_adjustments'] != 0) | (df['sold'] != 0)]
This state: if one of these value is not 0 then keep it, otherwise, filter out.
This will work too!!
df = df[(df[['purchase_qty', 'other_adjustments', 'sold']] != 0).any(axis=1)]
This is a fix to your method used:
df = df.drop(df[(df.purchase_qty == 0) & (df.other_adjustments == 0) & (df.sold == 0.0)].index)